This contribution features an accelerated computation of the Sinkhorn divergence by employing fast Fourier transforms. The algorithm proposed allows computing the Sinkhorn divergence by involving not more than O 𝑛(log 𝑛) 2 operations for probability measures supported by 𝑛 points. Further, the method avoids expensive allocations of the characterizing matrices. With this numerical acceleration, the transportation distance is accessible to probability measures out of reach so far. The method is particularly fast in lower dimensions.

This contribution features an accelerated computation of Sinkhorn's algorithm, which approximates the Wasserstein transportation distance, by employing nonequispaced fast Fourier transforms (NFFT). The proposed algorithm allows approximations of the Wasserstein distance by involving not more than O(n log n) operations for probability measures supported by n points. Furthermore, the proposed method avoids expensive allocations of the characterizing matrices. With this numerical acceleration, the transportation distance is accessible to probability measures out of reach so far. Numerical experiments using synthetic and real data affirm the computational advantage and superiority.